Start by clarifying requirements: input string format, Morse code mapping, and separator conventions (e.g., space between characters, slash between words). Then outline a solution using a hash map for encoding and string manipulation, and discuss edge cases like unsupported characters and case sensitivity.
Pro tip: Mention that in production, you'd use a precomputed lookup table and handle Unicode normalization; also note that Morse code is case-insensitive, so convert to uppercase first.
Ask about input constraints, expected output format, and separator conventions. Confirm handling of unsupported characters and spaces.
Use a hash map (dictionary) to map each character to its Morse code. Consider precomputing the mapping for efficiency.
Iterate through the input string, convert each character to Morse, and join with appropriate separators. Handle spaces as word separators.
Test with typical inputs, empty strings, mixed case, and unsupported characters. Verify output format matches requirements.
Talk about time/space complexity, potential optimizations, and how to handle errors or invalid input gracefully.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the exact Morse code format, including the separators for characters (e.g., spaces) and words (e.g., slashes). Then, build a reverse mapping from Morse code to characters and parse the input string by splitting on word and character separators, decoding each token. Finally, handle edge cases such as invalid codes, extra spaces, and empty input.
Pro tip: Mention that in production systems, you'd validate the Morse code against a standard mapping and consider using a trie for efficient decoding if the code is ambiguous or if you need to support variable-length codes. Also, discuss how you would test the solution with edge cases like leading/trailing separators and unknown codes.
Ask the interviewer to confirm the separator conventions (e.g., single space between characters, slash between words) and whether the input is guaranteed to be valid Morse code. Also, confirm the expected output format (e.g., uppercase letters, spaces between words).
Create a reverse mapping from Morse code to characters (e.g., '.-' -> 'A'). Split the input string into words using the word separator, then split each word into characters using the character separator, and map each Morse token to its corresponding character.
Consider cases like empty input, multiple consecutive separators, unknown Morse codes, and leading/trailing separators. Decide whether to throw an error, skip, or replace with a placeholder, and discuss this with the interviewer.
Write clean code with clear variable names and comments. Walk through a few examples, including a simple word and a sentence with multiple words, and test edge cases to ensure correctness.
State the time and space complexity (O(n) time, O(1) space for the mapping). Discuss potential optimizations, such as using a trie for decoding if the Morse code is not prefix-free or if you need to handle streaming input.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Model the problem as a search over all possible segmentations of the Morse string into valid words from the vocabulary. Use dynamic programming or backtracking with memoization to efficiently find a valid segmentation, leveraging the fact that Morse code has variable-length codes (1-4 symbols).
Pro tip: Clarify whether the output should be any valid word or all possible words; if multiple, discuss how to handle ambiguity (e.g., return the first, all, or use a language model to rank). Also, mention that precomputing a trie of Morse-encoded vocabulary can speed up matching.
Ask about input size, vocabulary size, whether multiple valid decodings exist, and if the output should be a single word or all possibilities. Confirm if the Morse string is guaranteed to be decodable.
Convert each word in the vocabulary to its Morse representation and build a trie for efficient prefix matching. This allows quick lookup of valid word prefixes during decoding.
Use DP where dp[i] stores whether the prefix up to index i can be segmented into valid words. Alternatively, use backtracking with memoization to explore all possible segmentations, pruning invalid paths early.
Code the solution, ensuring to handle edge cases (empty string, no valid segmentation). Optimize by using the trie to limit branching and memoizing results to avoid redundant computations.
Discuss time and space complexity: O(N * L) where N is Morse string length and L is max word length in Morse, or O(N^2) in naive DP. Compare with alternative approaches like BFS/DFS and explain why DP is preferred.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem: given a Morse string without separators and a vocabulary, find all valid sequences of words. Then describe a backtracking algorithm that recursively tries to match prefixes of the remaining Morse string against the Morse encodings of vocabulary words, using memoization to avoid redundant work. Finally, discuss trade-offs like time/space complexity and potential optimizations.
Pro tip: Mention that you would precompute a trie of Morse-encoded vocabulary words to efficiently prune invalid prefixes during backtracking, and discuss how memoization can reduce exponential blowup. This shows you think about both correctness and scalability.
Confirm that the input is a continuous Morse string (no spaces) and a vocabulary of words with known Morse encodings. Ask about output format (all possible sequences? count?) and constraints (length, vocabulary size).
Recursively try to match each vocabulary word's Morse code as a prefix of the remaining string. If it matches, recurse on the remainder and add the word to the current sequence. Base case: empty string yields a valid sequence.
Build a trie of Morse-encoded vocabulary words to quickly find all words that are prefixes of the remaining string. Use memoization (e.g., a map from index to list of sequences) to avoid recomputing subproblems.
Discuss worst-case time complexity (exponential without memoization, but bounded by number of valid sequences) and space complexity. Mention that memoization trades space for time and that the trie reduces constant factors.
Walk through examples, including cases with no valid sequences, multiple valid sequences, and ambiguous prefixes. Consider empty input and vocabulary words that are prefixes of others.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.